Update README.md

README.md

@@ -1,4 +1,25 @@
 ---
+license: apache-2.0
+task_categories:
+- automatic-speech-recognition
+- text-to-speech
+pretty_name: Nigerian Common Voice Dataset
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- en
+- ha
+- ig
+- yo
+multilinguality:
+- multilingual
+extra_gated_prompt: >-
+  By clicking on “Access repository” below, you also agree to not attempt to
+  determine the identity of speakers in the Common Voice dataset.
+size_categories:
+- 10K<n<100K
 dataset_info:
 - config_name: default
   features:
@@ -191,3 +212,164 @@ configs:
   - split: test
     path: yoruba/test-*
 ---
+# Dataset Card for Nigerian Common Voice Dataset
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+- [Additional Information](#additional-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Repository:** https://github.com/
+- **Point of Contact:** [Benjamin Ogbonna](mailto:[email protected])
+
+### Dataset Summary
+
+The Nigerian Common Voice Dataset is a comprehensive dataset consisting of 53 hours audio recordings and corresponding csv files. 
+This dataset also include metadata like accent, locale that can help improve the accuracy of speech recognition engines. This dataset is specifically curated to address the gap in speech and language 
+datasets for African accents, making it a valuable resource for researchers and developers working on Automatic Speech Recognition (ASR), 
+Speech-to-text (STT), Text-to-Speech (TTS), Accent recognition, and Natural language processing (NLP) systems.
+
+The dataset currently consists of 53 hours hours audio recordings in 4 languages, but more voices and languages are always added. Contributions are welcome.
+
+
+### Languages
+
+```
+English, Hausa, Igbo, Yoruba
+```
+
+## How to use
+
+The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. 
+
+For example, to download the Igbo config, simply specify the corresponding language config name (i.e., "igbo" for Igbo):
+```python
+from datasets import load_dataset
+dataset = load_dataset("benjaminogbonna/nigerian_common_voice_dataset", "igbo", split="train")
+```
+
+Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
+```python
+from datasets import load_dataset
+dataset = load_dataset("benjaminogbonna/nigerian_common_voice_dataset", "igbo", split="train", streaming=True)
+print(next(iter(cv_17)))
+```
+
+*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
+
+### Local
+
+```python
+from datasets import load_dataset
+from torch.utils.data.sampler import BatchSampler, RandomSampler
+dataset = load_dataset("benjaminogbonna/nigerian_common_voice_dataset", "igbo", split="train")
+batch_sampler = BatchSampler(RandomSampler(dataset), batch_size=32, drop_last=False)
+dataloader = DataLoader(dataset, batch_sampler=batch_sampler)
+```
+
+### Streaming
+
+```python
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+dataset = load_dataset("benjaminogbonna/nigerian_common_voice_dataset", "igbo", split="train")
+dataloader = DataLoader(dataset, batch_size=32)
+```
+
+To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
+
+### Example scripts
+
+Train your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 16 with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point comprises the `path` to the audio file and its `sentence`. 
+Additional fields include `accent`, `client_id` and `locale`.
+
+```python
+{
+  'client_id': 'user_5256', 
+  'path': 'clips/ng_voice_igbo_5257.mp3',
+  'audio': {
+    'path': 'clips/ng_voice_igbo_5257.mp3', 
+    'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32), 
+    'sampling_rate': 48000
+  },
+  'sentence': 'n'ihu ọha mmadụ.', 
+  'accent': 'nigerian', 
+  'locale': 'igbo', 
+}
+```
+
+### Data Fields
+
+`client_id` (`string`): An id for which client (voice) made the recording
+
+`path` (`string`): The path to the audio file
+
+`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
+
+`sentence` (`string`): The sentence the user was prompted to speak
+
+`accent` (`string`): Accent of the speaker
+
+`locale` (`string`): The locale of the speaker
+
+### Data Splits
+
+The dataset has been subdivided into portions for dev, train and test.
+
+## Data Preprocessing Recommended by Hugging Face
+
+The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. 
+
+Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
+
+In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
+
+```python
+from datasets import load_dataset
+ds = load_dataset("benjaminogbonna/nigerian_common_voice_dataset", "igbo")
+def prepare_dataset(batch):
+  """Function to preprocess the dataset with the .map method"""
+  transcription = batch["sentence"]
+  
+  if transcription.startswith('"') and transcription.endswith('"'):
+    # we can remove trailing quotation marks as they do not affect the transcription
+    transcription = transcription[1:-1]
+  
+  if transcription[-1] not in [".", "?", "!"]:
+    # append a full-stop to sentences that do not end in punctuation
+    transcription = transcription + "."
+  
+  batch["sentence"] = transcription
+  
+  return batch
+ds = ds.map(prepare_dataset, desc="preprocess dataset")
+```
+
+### Personal and Sensitive Information
+
+The dataset consists of people who have donated their voice online.  You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
+
+### Social Impact of Dataset
+
+The dataset consists of people who have donated their voice online.  You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
+
+### Contributions